Method, system, and storage medium for facilitating excess inentory utilization in a manufacturing environment

ABSTRACT

An exemplary embodiment of the invention relates to a storage medium for facilitating excess inventory utilization. The excess inventory includes parts used in end products. The storage medium includes instructions for a causing a computer to implement a method. The method includes translating sales specific terminology describing the end products into bill of material terminology describing the parts used in end products via a bill of material structure. The translating includes mapping between features associated with end items and parts required to build the end items. The method also includes determining an optimal build plan for the end items that, if built, would consume a desired quantity and/or type of excess inventory.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.10/210,432, filed Aug. 1, 2002, the disclosure of which is incorporatedby reference herein in its entirety.

FIELD OF THE INVENTION

The present invention relates generally to inventory management. Moreparticularly, the present invention relates to a method, system, andstorage medium for facilitating excess inventory utilization in amanufacturing environment.

BACKGROUND OF THE INVENTION

In a manufacturing environment products are built that often comprisemany possible configurations while sharing some common lower levelassemblies and parts in their bills of material. Due to factors such asmarket fluctuations and other unanticipated environmental changes, it isnot uncommon for a manufacturer to be left with excess inventory oncertain parts. These parts may include those that are ordered but notyet received in which the manufacturer incurs liability forcancellation, or they may be parts held by a contract manufacturer, orinvolve other similar types of situations.

Surplus/excess inventory can be problematic for a manufacturer as it canincrease costs and reduce profits. Inventory specialists are continuallyworking to reduce inventory levels by finding ways to shorten thepipeline and reduce lead times. Attempts at solving the surplus/excessinventory problem include developing a build plan for end products thatwould consume as many of these surplus/excess parts as possible. Thisbuild decision has been solved in the past in a manual fashion byinvestigating individual choices one at a time. Not only is this laborintensive, but when an excess parts could be used on several alternativesaleable items, each of which might consume various quantities of otherexcess parts, the problem becomes too complex for the manual approach.

What is needed is a method of assessing existing surplus/excess partsinventories and developing an optimum build plan for end products thatwould consume these parts.

SUMMARY OF THE INVENTION

An exemplary embodiment of the invention relates to a storage medium forutilizing excess inventory comprising parts used in end products. Thestorage medium includes instructions for a causing a computer toimplement a method. The method includes translating sales specificterminology describing the end products into bill of materialterminology describing the parts used in end products via a bill ofmaterial structure. The translating includes mapping between featuresassociated with end items and parts required to build the end items. Themethod also includes determining an optimal build plan for the end itemsthat, if built, would consume a desired quantity and/or type of excessinventory.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary system for facilitating excessinventory utilization management.

FIG. 2 is a graphical depiction of a sample bill of material structureused in a translation process of the inventory utilization tool.

FIG. 3 is a flowchart of a process for assisting an inventory planner indetermining an optimum build plan in an exemplary embodiment.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a block diagram of an exemplary system for facilitating excessinventory utilization in accordance with an exemplary embodiment of theinvention. Excess parts files (also referred to herein as excessinventory files) 106 are provided by manufacturing/inventory personneland contain lists of any excess/surplus (herein referred to as ‘excess’)parts along with the quantity in excess. Manufacturing process datafiles 107 may include data relating to yields, cycle times, usage rates,capacities, alternative parts, and other desirable information. Datafrom these files 106 and 107 are submitted to a Manufacturing ResourcePlanning (MRP) engine 102 that employs the inventory utilization tool104 of the invention. BOM files in database 108 store parts lists forsaleable end items. Saleable end items refers to parts, products,subassemblies, and includes any item that can be produced and sold forrevenue by a manufacturing entity in order to use up excess inventory.The inventory utilization tool 104 comprises a shell script forgenerating new BOM data structures as will be discussed further herein.A reports database 110 is included in FIG. 1 and stores reportsgenerated by the inventory utilization tool 104 for analysis and reviewby specified inventory specialists (also referred to herein as inventoryplanners).

Operation of the inventory utilization tool from the perspective of aninventory planner of a manufacturing entity will now be described withreference to FIGS. 2 and 3. In an exemplary embodiment, the inventoryutilization tool employs a method comprising a translation segment and asolution segment.

For products (also referred to herein as saleable end items) that arefairly complex, and the configuration choices numerous, a simplified wayof describing them may be helpful so that customers and salespeople ofthe manufacturing entity have an easier time deciding on and orderingwhat they want. This ‘sales nomenclature’ involves a mapping process attime of order placement whereby products are transcribed fromsales-specific terminology to bill of material part numbers (referred toherein as ‘manufacturing nomenclature’). The mapping is preferablymany-to-many, and may often be order dependent (i.e., one-way mapping).

For example, in placing an order for an upgraded automobile interior,the dealer may specify a code ‘DLUX’. This code is the same for model X(economy car), model Y (luxury sedan), or model Z (sports utilityvehicle). However, the part numbers required for that order (i.e., partnumbers that are needed in order for manufacturing to fulfill the order)are dependent on the code ‘DLUX’ as well as the model number of thevehicle (i.e., model X, Y, or Z).

There are two decisions this method supports: (1) which saleable enditems to produce out of the excess inventory in order to use up as muchinventory as possible while incurring minimal costs for purchasingadditional lower-level parts needed to ‘square up sets’ and produce theend items; and (2) how to express the choice of saleable end items insales nomenclature, even though the available inventory is expressed inmanufacturing nomenclature.

The translation segment takes information presented in manufacturingterms and translates it into sales terms. Product descriptions ofsaleable end items using sales nomenclature are referred to herein as‘features’. Saleable end items include items which can be sold forrevenue. A personal computer is one example of a saleable end item. Somefeatures may require the same part numbers, regardless of other aspectsof the order (e.g., a replacement headlight might be referred to as“HDLT” and would include a distinct set of parts to build). Thesefeatures are referred to herein as ‘simple features’.

Features that require just one other piece of information in order toknow the exact part numbers required are referred to as ‘plus-onesimple’ features. Plus-one simple features become ‘simple’ with theaddition of one piece of data. For example, an automobile exhaust systemrequires knowing the state in which the vehicle will be registered dueto differing emission controls among the states. The exhaust system maybe referenced as ‘EXHST’ with a plus-one feature for the state.

The bill of materials structure for plus-one simple features can bebuilt by making a copy of the feature for each distinct value of theadditional information (i.e., distinct in the sense that different partsare required). For example, if California required different parts inits exhaust systems but all other states had the same parts, twosaleable assembly parts would be created out of the one feature asillustrated in FIG. 2.

This concept may be extended to ‘plus-two simple’ features, ‘plus-threesimple’ features, and so on. The saleable assembly parts created out ofa ‘plus-k simple’ feature would be k-tuples, resulting in potentiallyextensive proliferation of similar bill records as ‘k’ gets large.

This same approach may extend to features whose translation depends onthe presence of other features. For example, a deluxe interior(described as one feature) coupled with a stereo (described as a secondfeature) may require a different dashboard cover (identified by adifferent part number) than a deluxe interior without that stereo.

In practice, it makes sense to capture those features which are assimple as possible, yet make up the vast majority of all features.Plus-one or plus-two simple features may be all that are necessary todescribe these features. Any other or more complex features could beomitted from the build-out decision, unless they can be expressed in adifferent way.

Once the new BOM structure is established, the tool assists the MRPengine in the solution segment as described herein.

The decision of what to build out of excess parts requires a way todistinguish value between choices of saleable end items, often expressedas either revenue or profit. This decision is very similar to thedecision of what can best be built out of a set of constrained parts.This latter problem has been modeled in various ways and solved bylinear programming (LP) tools as well as various heuristics (e.g., localsearch techniques), artificial intelligence techniques (e.g., geneticalgorithms, neural networks, tabu search, etc.), and non-linearoptimization (e.g., gradient search techniques). One such approachinvolves an optimization engine which uses a set of subroutines calledthe Watson Implosion Tool (WIT). This approach is the subject of U.S.Pat. No. 5,630,070, entitled “Optimization of Manufacturing ResourcePlanning”. The model of the present invention differs in that an apriori expression of demand that a manufacturer is trying to satisfy isnot needed. Also, a manufacturer may wish to express an upper limit(referred to as ‘k’) on how much to spend for additional materials inorder to use up one dollar of excess inventory. Further, revenues perunit of a saleable item (i.e., prices) may be volume-dependent, a factoraddressed by the present invention. Finally, higher-level (e.g.,assembly) items in excess may also be disassembled to create and thenreuse supply of these low-level parts in other products.

The method involves getting all data related to the manufacturingprocess (e.g., yields, cycle times, usage rates, capacities, alternateparts, etc.) from manufacturing process database 107 or other similardata source and formulate constraints related to the usage of materialsin producing other materials, and eventually, saleable end items. If norevenue data are available, the choice amongst saleable end items can bemade by using a formula, ‘k’ times ‘ei’ (expressed as k*ei), as therevenue for item ‘i’ where ‘ei’ is the amount in dollars of excessinventory consumed when producing/selling one unit of item ‘i’. Thescalar factor ‘k’ referenced above may be set by a decision maker. Ei iscalculated by using a ‘pegging’ subroutine applied to a variant of theoriginal problem, where a dummy part is added as a component of allother parts, with usage rate set to the value of the part for those thatare excess inventory, and to zero otherwise. This step assists inbalancing two competing forces: how important is using up excess (at anycost) versus how important is it to not incur additional cost (forsquaring up sets). This balance is reflected through ‘k’, whichindicates how much one is willing to spend (in additional purchases) ata maximum in order to use of one dollar of excess. For example, amanufacturing entity may be interested in spending $1.00 for everydollar of excess inventory to be disposed. Alternatively, amanufacturing entity may be more interested in getting rid of inventoryand may choose to spend $3.00 to dispose of a $1.00 of excess inventory.The ‘k’ factor expresses this sensitivity between using up excess andminimizing additional spending.

In order to find the most desirable solution for a given value of ‘k’,the problem may be modeled as a linear equation as follows.

Maximize profit=Revenue minus Cost

It will be noted that the cost to build a saleable end item is the costof all components where those in excess are considered free, and therevenue is ‘k’ times the excess consumed in building one end item. Thisequation assumes that the price the marketplace would pay for the itemis not known. If actual prices are known, they can be substituted forrevenue in the above equation. Thus, using the scaling factor, one canoptimize how much excess to be consumed (e.g., setting ‘k’ very large),or how little additional purchase required (e.g., setting ‘k’ verysmall), or somewhere in between. Alternatively, if market prices areavailable, they may be utilized in the equation in order to optimizeoverall profit.

The optimization objective may be more general than profitability, forexample, by factoring in a priority on the saleable end items thatreflects how perishable they are, how easy they are to sell, howseasonable they may be, etc. By way of example and not limitation, otheroptimization objectives may include or take into account strategicobjectives (e.g., a loss leader), special customer service needs, or anyother desired business purpose. This information can be most generallyconsidered “business impact” information, which can be balanced withother factors such as the relative desirability of building end items,with the relative cost of additional purchases needed, and the benefitof consuming the excess inventory. It is generally known in the art thatthere are situations influencing production decisions beyond mechanicalanalysis of simple profitability defined by revenue minus costs of anindividual end item.

Note that this business impact can also vary over time and by volume.For example, the company might be able to sell 100 units at $5,000 eachbut selling more than 100 units would only be possible if the price weredropped to $4,800. In this example, business impact would be defined asprofit (e.g., end product revenue minus the cost of parts in theassociated BOM, cost of manufacturing, etc.), and the revenues forsaleable end items would be defined by piece-wise linear functions ofthe volume sold. Since some of the parts in excess inventory (as well asthe end products and end items to be made from such parts) may losevalue more quickly than others (i.e., more “perishable”), there may be ahigher priority associated with particular parts in excess inventory orend products which will in turn affect the optimization of the buildplan.

Similarly, the costs of additional purchase items may depend on time orvolume. Additionally, by treating an excess inventory item or part as“saleable” (that is, it could be sold by itself, or built into ahigher-level product), one can also capture the dynamic oftime-dependent scrap value (perishability). Finally, some of the itemsrequired for building a saleable end item may be constrained (e.g., havean ‘infinite’ cost to acquire beyond a certain number of units). All ofthese considerations on ‘business impact’ can be taken into account infinding an optimal solution.

The optimization criteria used in the optimization are non-demandcriteria. Non-demand criteria are those criteria that can be assessedwithout having a demand statement associated with one or more endproducts. Demand statements are a quantity that is forecasted orconfirmed of how many end products can be sold. In many instances,demand statements are broken out over units of time (e.g., 10 of product‘X’ in January, 50 of product ‘Y’ in 2002). In configurable products,such as high end computer systems, it is difficult to develop demandstatements on every permutation. Examples of non-demand criteria are:utilizing the quantity or dollar value of inventory, minimizingadditional dollars spent to utilize quantity or dollar value ofinventory, maximizing profit of end items, taking into account otherbusiness criteria such as perishability, a loss leader, etc., oranything else that does not include a demand statement.

In MRP systems and other support tools for helping companies decide whatto build, demand for end products is a critical, if not the mostcritical criteria for optimizing a build plan. The intent is to producewhere there is a known demand as represented by demand statements forthe end products that the business entity produces.

When focusing on surplus/excess parts inventory, the optimization of thebuild plan will focus on criteria relevant to the surplus/excessinventory such as the quantity or dollar value of inventory consumed bythe build plan or the overall potential business impact of the endproducts produced. These decisions are initially made absent demandstatements and the build plan is assessed for ease of sale or as a guideto areas where steps should be taken to increase demand. In other words,the present invention assists in generating opportunities while existingMRP systems and support tools typically match supply to a proposeddemand for end products. One way of using the exemplary embodiment wouldbe to generate a number of possible build scenarios optimized usingnon-demand criteria and having marketing professionals do demandanalysis or other analysis to determine which of the opportunitiesidentified would be easiest to execute.

The process of utilizing the above optimization component in amanufacturing setting will now be described.

Basic records and inputs are obtained at step 302. These include BOM,costs, revenue (if available), inventory, and ei data. The tool sets kto zero and ‘DemandVolumes’ to ‘Big M’ at step 304. ‘Big M’ refers to aninitial upper limit in linear programing and is used as an initialsolution (e.g., a demand to satisfy) in starting the optimizationprocess. This value, or upper limit, is selected for the purpose ofinitiating implementation of the linear program only and does notreflect actual or specific demand values (e.g., a random or arbitraryvalue). An implosion tool is used to find optimal available-to-sell(ATS) volumes for end-items at step 306. The saleability of ATS volumesare assessed at step 308. If ATS volumes exceed saleability at step 310,then ‘DemandVolumes’ for those items are decreased at step 312 and theprocess returns to step 306. Steps 306-308 are repeated until ATSvolumes are less than or equal to saleability. Aggregated availabilityis assessed at step 314. If aggregated availability is exceeded for anyitem groups at step 316, then demand volumes for items within this groupare decreased at step 318 and the process again returns to step 306;otherwise, the process continues at step 319 in which the consumption ofexcess items is assessed. If more excess needs to be consumed, then k isincremented at step 320 and the process returns to step 306. Steps306-319 may be repeated until there is no more excess requiringconsumption.

The present invention enables a manufacturer to pursue a concertedeffort in sales and to sell through items which will consume excessinventory. These efforts may include alternate channels (such ase-auctions or discount brokers), marketing campaigns, use as repairparts, etc. Furthermore, the approach to translating from manufacturinginto sales nomenclature has further applications to expressingavailable-to-promise (ATP) in terms meaningful to the sales force andcustomers.

As described above, the present invention can be embodied in the form ofcomputer-implemented processes and apparatuses for practicing thoseprocesses. The present invention can also be embodied in the form ofcomputer program code containing instructions embodied in tangiblemedia, such as floppy diskettes, CD-ROMs, hard drives, or any othercomputer-readable medium, wherein, when the computer program code isloaded into and executed by a computer, the computer becomes anapparatus for practicing the invention. The present invention can alsobe embodied in the form of computer program code, for example, whetherstored in a storage medium, loaded into and/or executed by a computer ortransmitted over some transmission medium, such as over electricalwiring or cabling, through fiber optics, or via electromagneticradiation, wherein, when the computer program code is loaded into andexecuted by a computer, the computer becomes an apparatus for practicingthe invention. When implemented on a general-purpose microprocessor, thecomputer program code segments configure the microprocessor to createspecific logic circuits.

It will be evident to those skilled in the art that the presentinvention provides many improvements over the current state of the artof ratio planning. Data from a variety of systems and locations is beingcollected into a single database in order to provide a single,integrated repository for ratio planning data. The invention allowsratio planners to catalogue part numbers and models in order to providesome structure and meaning to the thousands of seemingly random partnumbers. The cataloging provides an easy way to pull informationtogether for reports. Additionally, the invention provides the ability,through the use of pre-defined reports, to generate reports very quicklyand with a minimum of computer database expertise on the part of theratio planner. The invention is well suited for both small manufacturerswith relatively few ratios as well as very large manufacturers with tensof thousands of ratios.

While the invention has been described with reference to exemplaryembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications may be made to adapt a particular situationto the teachings of the invention without departing from the essentialscope thereof. Therefore, it is intended that the invention not belimited to the particular embodiments for carrying out this invention,but that the invention will include all embodiments falling within thescope of the appended claims.

1. A storage medium comprising machine readable computer program codefor utilizing excess inventory in a manufacturing environment, saidexcess inventory comprising parts used in end products, said storagemedium including instructions for a causing a computer to implement amethod, comprising: translating sales specific terminology describingsaid end products into bill of material terminology describing saidparts used in end products via a bill of material structure, saidtranslating including mapping between features associated with end itemsand parts required to build said end items; and utilizing said bill ofmaterial structure, determining an optimal build plan for said end itemsthat, if built, would consume a desired quantity and/or type of excessinventory; wherein said determining an optimal build plan includes:formulating constraints related to usage of parts in producing end itemsutilizing: data provided in said bill of material structure; andmanufacturing process data resulting in at least one saleable end item,said manufacturing process data comprising at least one of: costs;revenue data; inventory data; usage rates; yields; capacities; cycletimes; and alternate parts; and optimizing said constraints utilizing atleast one of: heuristics; linear programming; artificial intelligence;and non-linear optimization; wherein selecting an end item from a listof saleable items capable of consuming excess inventory comprises:applying an algorithm comprising: k*ei; wherein k is a scalar factorreferring to a selectable number operable for expressing an upper limiton how much to spend on additional parts in order to use up one dollarof excess inventory; and wherein further, ‘ei’ represents an amount indollars of excess inventory consumed when producing one unit of an item‘i’.
 2. The storage medium of claim 1, wherein said mapping includes:sub-mapping said features wherein a feature of an end item comprisesmore than one distinct set of parts and parts required to build said enditems.
 3. The storage medium of claim 1, wherein selecting an end itemfrom a list of saleable end items capable of consuming excess inventoryfurther includes: utilizing user-supplied pricing information todetermine which of said saleable end items, if built, would consume themost excess parts while requiring minimal additional expense.